搜索资源列表
svm
- svm 实现,经典的SMO算法，支持三种kernel（多项式，高斯，内积）。还有test 函数 -implmentation of svm
svm
- 对支持向量机硬间隔，软间隔和核函数的实现，采用的核为高斯核，高斯核所需的数据集包含在data中。-Support vector machines for hard intervals, soft interval and kernel implementations, using Gaussian kernel by kernel, Gaussian kernel required data set contains the data in.
SVM
- 為支持向量機之函式庫，可做分類應用，內部可選擇分類器的類型及核函數，對於學習支持向量機有很大的幫助-Support vector machines for the library, do classification applications, optional internal classification of the type and kernel function for support vector machine learning of great help
svm
- Kernel-based Virtual Machine driver for Linux.
kernel
- svm多种分类的核函数。可以实现，简单好用。-Kernel svm multiple classification. Can be achieved, easy to use
chapter13_GA
- 对最小二乘支持向量机的核函数进行参数优化，最后得到分类更加准确的分类器-For least squares support vector machine (SVM) kernel function parameter optimization, finally get a more accurate classification of classifier
static_SVM_line_mean_var
- MATLAB代码，利用支持向量机SVM，核函数为线性核函数并进行参数寻优对数据进行分类-MATLAB code, the use of support vector machine SVM, kernel function is linear kernel parameter optimization and data classification
Genetic-algorithm-to-optimize-svm
- 遗传算法优化支持向量机的惩罚系数C与高斯核系数g，能够提高支持向量机的分类精度-Genetic algorithm to optimize the punish coefficient of support vector machine (SVM) with gaussian kernel coefficient C g, can improve the support vector machine (SVM) classification accuracy
SVM
- classify using one-against-one approach, SVM with linear, 3rd degree poly,RBF 7 kernel
influence-of-parameter-in-SVM
- 验证SVM中惩罚系数C与高斯核宽度系数g对于其分类性能的影响-Test the penalty parameter C and the the gaussian kernel factor g for classification influence of SVM
svm
- 1．掌握支持向量机（SVM）的原理、核函数类型选择以及核参数选择原则等； 2．熟悉基于libSVM二分类的一般流程与方法；-1. Master support vector machine (SVM) principles, and the type of kernel function kernel parameter selection principles 2. Familiar with the process-based approach libSVM two classifi
SVM
- 支持向量机分类程序，使用高斯核函数，SMO顺序最优化算法，为学习SVM提供参考-SVM program, using a Gaussian kernel, SMO sequence optimization algorithm to provide a reference for learning SVM
pso-svm-prediction
- 该程序是基于粒子群算法优化支持向量机中的正则化参数C和核函数参数K的算法，实现了对电力负荷的短期预测，预测效果较好，可根据自己要求进行更改。-The algorithm is based on particle swarm optimization algorithm to optimize regularization parameter C and kernel function parameter K in support vector machine. It realizes the s
svm
- 基于RBF径向基核函数实现SVM支撑矢量机算法使用RBF，garma值为0.5-Based on RBF radial basis kernel function to achieve SVM support vector machine algorithm using Garma, RBF value of 0.5
svm
- SVM ： /kernel.m /main.m /svmTest /svmTrain.m 亲测可用，直接运行main函数-SVM ： /kernel.m /main.m /svmTest /svmTrain.m
SVM-with-Examples
- 介绍了支持向量机和其他基于学习的方法,相关人员可以参考。-Introduction to support vector machines and other kernel-based learning methods
D2dataset-master
- Perform SVM and test results a) For now, assume that the main scr ipt is grid_search.IT performs a grid search for SVM using RBF kernel , for the 2 parameters C and gamma .Criterios for choosing the right values is the F1 score and accuracy of the
SVM-KMExample
- examples of SVM, PCA , MultiSVM, Feature extraction, kernel function
svm_NonLinear
- 非线性的SVM，利用核函数对所归一化后数据进行处理，得到分类结果(svm_NonLinear Nonlinear SVM, using the kernel function to normalize the data to be processed, the classification results)
SVMcg
- LIBSVM的参数寻优，主要是自动计算惩罚系数和核函数中的gamma函数(The parameter optimization of LIBSVM is mainly to automatically calculate the penalty coefficient and the gamma function in the kernel function)